We showed results on pruning at different granularities as well as their multiple combinations. We showed that by explicitly modeling pruning at these granularities, the network tries to increase the level of pruning without significant loss in accuracy. In future we aim to extend our scope of analysis to bigger datasets such as CIFAR-100 and on more complicated networks such as Inception-ResNets [1]}. We will also be analysing the effect of using different schemes of regularizations to identify the best at each granularity.
